![]() ![]() ![]() To measure multicollinearity, you can examine the correlation structure of the predictor variables. Coefficients of the highly correlated terms can even have the wrong sign. ![]() Removing any highly correlated terms from the model will greatly affect the estimated coefficients of the other highly correlated terms.Coefficients for highly correlated predictors will vary widely from sample to sample.Coefficients can seem to be insignificant even when a significant relationship exists between the predictor and the response.The following are some of the consequences of unstable coefficients: Severe multicollinearity is problematic because it can increase the variance of the regression coefficients, making them unstable. Multicollinearity in regression is a condition that occurs when some predictor variables in the model are correlated with other predictor variables.
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